Plotting function to reduce code length + simplify. Then, short function to place MLS weight smoothing kernel into same category to make visually consistent, updates are:

  • 3.0 –> 3.6
  • 4.0 –> 4.8
  • 5.0 –> 6.0
  • 6.0 –> 7.2
  • 7.0 –> 8.4
create_plot <- function(dataset, type, thresh) {
  plot <- dataset %>%
    ggplot(aes(x = .data[[type]], y = median_est, fill = .data[[type]], color = .data[[type]])) +
    geom_rain(alpha = .5, rain.side = 'l',
              boxplot.args = list(color = "black", outlier.shape = NA),
              boxplot.args.pos = list(
                position = ggpp::position_dodgenudge(x = .1), width = 0.1
              )) +
    facet_grid(~study) +
    theme_classic() +
    ggtitle(paste("Distribution by",type,"category (",thresh,"mask)")) +
    scale_fill_manual(values = pal) +
    scale_color_manual(values = pal) +
    guides(fill = 'none', color = 'none') +
    theme(text = element_text(family = "Times New Roman"),
          axis.text = element_text(size = 10, angle = 45, hjust = 1),
          axis.title = element_text(size = 10),
          legend.text = element_text(size = 10),
          legend.title = element_text(size = 10),
          plot.title = element_text(size = 16))
  
  return(plot)
}

update_mls_fwhm <- function(df) {
  df <- df %>%
    mutate(fwhm = case_when(
      fwhm == "fwhm-3.0" ~ "fwhm-3.6",
      fwhm == "fwhm-4.0" ~ "fwhm-4.8",
      fwhm == "fwhm-5.0" ~ "fwhm-6.0",
      fwhm == "fwhm-6.0" ~ "fwhm-7.2",
      fwhm == "fwhm-7.0" ~ "fwhm-8.4",
      TRUE ~ as.character(fwhm)  # if none of the conditions match, keep the original value
    ))
  return(df)
}

create_specr_plot <- function(summary_df, est_label) {
  plot_a = plot_curve(df = summary_df, ci = TRUE, desc = FALSE, legend = FALSE, null = 0)
  plot_b <- plot_choices(df = summary_df, choices = c("fwhm", "motion","contrast","model"), desc = F, null = 0) +
    labs(y = "Variables", x = paste("Ordered Specification Curve \n",est_label, "coefficient"))
  
  cowplot::plot_grid(plot_a, plot_b, ncol = 1, align = "v", axis = 'tblr',
                     labels = c('A', 'B'), rel_heights = c(1, 2),
                     label_fontfamily = "Times", label_size = 12)
}

calculate_summary_stats <- function(data, variable, est_type) {
  data %>%
    select(study, {{variable}}) %>% 
    group_by(study) %>% 
    summarise(
      "est_type" = est_type,
      "median" = median({{variable}}),
      "sd" = sd({{variable}}),
      "min" = min({{variable}}),
      "max" = max({{variable}}) 
    ) %>% 
  kbl(format = "html", 
      booktabs = TRUE) %>% 
  kable_styling(full_width = FALSE)
}

The packages are automatically loaded using pacman. The reported .html was last ran on the system: x86_64-apple-darwin17.0 and R version: R version 4.2.1 (2022-06-23) In the Stage 1 PCI Registered Report we are focused on Individual Continues (intraclass correlation) and the binary/continuoues group similarity (jaccard and spearman). Related to the descriptive file here, we describe each step that contained within this file that is relevant to the registered analyses

continuous

We stated:

Aim1: the range and distribution of median ICCs across each study (three) and analytic decision category (four) are plotted across suprathreshold task-positive and subthreshold ICCs using Rainclouds (Allen et al., 2019) and the median and standard deviation is reported in a table.

to visualize the ordered median ICCs across the 360 model permutations for suprathreshold task-positive and subthreshold ICCs, specification curve analyses are used (Simonsohn et al., 2020). Specifically, results across the 360 model permutations are reported using a specification curve to represent the range of estimated effects across the variable permutations. This consists of two panels: Panel A represents the ordered ICC coefficients and the ICC’s associated 95% confidence interval colored based on no significance (gray), negative (red) or positive (blue) significance from the Null (Null here is 0) and Panel B represents the analytic decisions from each of the four categories (see Table 1) that produced the median ICC estimates. In the main text, to compare the highest and lowest ICC’s produced by the model permutations, the 25th percentile and 75th percentile median ICC estimates from the 360 models are reported separately for suprathreshold task-positive and subthreshold activation (the specification curve for all ICC estimates for suprathreshold task-positive and subthreshold activation are provided as supplemental information).

Aim2: the range and distribution of median MSBS and MSWS across each study and analytic decision category are plotted across suprathreshold task-positive and subthreshold ICCs using Rainclouds.

two separate specification curve analyses report the ordered median MSBS and MSWS coefficients in one panel and the analytic decisions that produced the MSBS and MSWS estimates in a second panel separately for suprathreshold task-positive and subthreshold activation

group similarity

We stated:

Aim1: For each study, the coefficients are plotted to reflect the distribution and range of coefficients. Both Jaccard’s and Spearman correlation are reported separately.

1 Load data

1.1 ABCD

1.2 AHRB

1.3 MLS

1.4 combine data

2 Sample descriptives

2.1 ABCD

# ABCD NDA DATA Info
abcd_nda$subject <- abcd_nda$participant_id
abcd_nda$subject <- gsub("_", "", abcd_nda$subject)
abcd_nda$session <- abcd_nda$eventname
abcd_nda$session <- gsub('2_year_follow_up_y_arm_1', '2YearFollowUpYArm1', abcd_nda$session)
abcd_nda$session <- gsub('baseline_year_1_arm_1', 'baselineYear1Arm1', abcd_nda$session)
abcd_nda_subset <- abcd_nda %>% 
  filter(subject %in% abcd_site13_ids$V1) %>% 
  select(!c(subjectkey,eventname))
abcd_days <- abcd_nda_subset %>% 
  mutate(date_r = as.Date(interview_date, format = "%m/%d/%Y")) %>% 
  group_by(subject) %>% 
  arrange(date_r) %>% 
  mutate(days_btwn_scans =  as.integer(diff(date_r))) %>% 
  select(days_btwn_scans, subject, interview_age,sex) 

colnames(abcd_days) <- c("days","subject","Age","Sex")
abcd_days$sample <- "ABCD"
rm(abcd_nda,abcd_site13_ids)

2.2 AHRB

ahrb_demo_subset <- ahrb_demo %>% 
  filter(participant_id %in% ahrb_ids$V1) %>% 
  mutate(days = days_ses1toses2)
ahrb_demo_subset$sample <- "AHRB"
rm(ahrb_demo, ahrb_ids)

2.3 mls

mls_demo_subset <- mls_demo %>% 
  filter(participant_id %in% mls_ids$V1) %>% 
  mutate(days = if_else(is.na(reward_days_ses1toses2),
                        reward_ses2toses3,reward_days_ses1toses2)
  ) %>% 
  filter(session==1) %>% 
  rename()
mls_demo_subset$sample <- "MLS"
rm(mls_demo, mls_ids)
dur_scans <- rbind(abcd_days %>% select(subject,days,sample) %>% unique(),
                   ahrb_demo_subset %>% rename("subject" = participant_id) %>% select(subject,days,sample),
                   mls_demo_subset %>% rename("subject" = participant_id) %>% select(subject,days,sample))

2.4 Descriptives

Plot distribution of days between scans

dur_scans %>% 
  ggplot(aes(x = subject, y = days, fill = sample, color="white")) +
  geom_bar(stat = 'identity', position = 'dodge') +
  ylab("Days between scans") +
  xlab("") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = "white") +
  theme_minimal() +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), text = element_text(family = "Times New Roman")) +
  facet_grid(~sample, scales = 'free_x') +
  guides(fill = FALSE, color = FALSE, scale = "none")
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

dur_scans %>%
    group_by(sample) %>% 
    summarise(
      "Sample (N)" = n(),
      "Avg Days Between Scans" = mean(days),
      "SD Days Between Scans" = sd(days)
    ) %>% 
  kbl(format = "html", 
      booktabs = TRUE) %>% 
  kable_styling(full_width = FALSE)
sample Sample (N) Avg Days Between Scans SD Days Between Scans
ABCD 130 745.9538 78.92837
AHRB 60 419.4167 80.13660
MLS 81 1088.7037 623.50815 
demo_comb <- rbind(abcd_days %>% distinct(subject, .keep_all = TRUE) %>% 
                     mutate(sample = "ABCD", Age = round(Age/12,2)) %>% select(sample, subject,Age, Sex, days),
                   ahrb_demo_subset %>% 
                     mutate(sample = "AHRB", Sex = if_else(sex==0,"F","M")) %>% 
                     rename("Age"= age, "subject" = participant_id) %>% select(sample, subject,Age,Sex, days),
                   mls_demo_subset %>% 
                     mutate(sample = "MLS", Age = round(if_else(is.na(reward_days_ses1toses2),
                                                                ((ScanAge * 365) + reward_ses2toses3) / 365,
                                                                ScanAge),1),
                            Sex = if_else(Sex==1,"F","M")) %>% 
                     rename("subject" = participant_id) %>% select(sample, subject,Age,Sex, days) 
                   ) 


table1(~Age + factor(Sex) + days | sample, data = demo_comb)
ABCD
(N=130)		 AHRB
(N=60)		 MLS
(N=81)		 Overall
(N=271)
Age
Mean (SD) 9.81 (0.573) 19.3 (1.30) 20.7 (2.26) 15.1 (5.35)
Median [Min, Max] 9.83 [9.00, 11.0] 19.2 [17.2, 21.4] 20.2 [18.0, 26.8] 17.5 [9.00, 26.8]
factor(Sex)
F 70 (53.8%) 35 (58.3%) 31 (38.3%) 136 (50.2%)
M 60 (46.2%) 25 (41.7%) 50 (61.7%) 135 (49.8%)
days
Mean (SD) 746 (78.9) 419 (80.1) 1090 (624) 776 (421)
Median [Min, Max] 725 [648, 1070] 391 [352, 692] 756 [332, 2970] 712 [332, 2970]

3 Plot distributions

Below, running the steps to summarize the different Intraclass correlation (ICC), Mean Squared Between Subject (MSBS), Mean Square Within Subject (MSWS), and jaccard and spearman similarty for the model combinations 360 across samples

3.1 ICC

Plotting overall and for each of [four] categories

3.1.1 Subthreshold Mask

Creating rainclouds via ggrain

fwhm_rg = create_plot(icc_subthresh, "fwhm","sub-threshold")
motion_rg = create_plot(icc_subthresh, "motion","sub-threshold")
modeltype_rg = create_plot(icc_subthresh, "model","sub-threshold")
contrast_rg = create_plot(icc_subthresh, "con","sub-threshold")

fwhm_rg
## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

motion_rg

modeltype_rg

contrast_rg

3.1.2 Suprathreshold Mask

fwhm_rg = create_plot(icc_suprathresh, "fwhm","supra-threshold")
motion_rg = create_plot(icc_suprathresh, "motion","supra-threshold")
modeltype_rg = create_plot(icc_suprathresh, "model","supra-threshold")
contrast_rg = create_plot(icc_suprathresh, "con","supra-threshold")

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.2 MSBS

Plotting overall and for each of [four] categories

3.2.1 Subthreshold Mask

fwhm_rg = create_plot(bs_subthresh, "fwhm","sub-threshold")
motion_rg = create_plot(bs_subthresh, "motion","sub-threshold")
modeltype_rg = create_plot(bs_subthresh, "model","sub-threshold")
contrast_rg = create_plot(bs_subthresh, "con","sub-threshold")

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.2.2 Suprathreshold Mask

fwhm_rg = create_plot(bs_suprathresh, "fwhm","supra-threshold")
motion_rg = create_plot(bs_suprathresh, "motion","supra-threshold")
modeltype_rg = create_plot(bs_suprathresh, "model","supra-threshold")
contrast_rg = create_plot(bs_suprathresh, "con","supra-threshold")

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.3 MSWS

Plotting overall and for each of [four] categories

3.3.1 Subthreshold Mask

fwhm_rg = create_plot(ws_subthresh, "fwhm","sub-threshold")
motion_rg = create_plot(ws_subthresh, "motion","sub-threshold")
modeltype_rg = create_plot(ws_subthresh, "model","sub-threshold")
contrast_rg = create_plot(ws_subthresh, "con","sub-threshold")

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.3.2 Suprathreshold Mask

fwhm_rg = create_plot(ws_suprathresh, "fwhm","supra-threshold")
motion_rg = create_plot(ws_suprathresh, "motion","supra-threshold")
modeltype_rg = create_plot(ws_suprathresh, "model","supra-threshold")
contrast_rg = create_plot(ws_suprathresh, "con","supra-threshold")
fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.4 Similarity

3.4.1 jaccard

fwhm_rg = similarity_df %>% ggplot(aes(x = fwhm, y = jaccard, fill = fwhm, color = fwhm)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  theme_classic() +
  facet_grid(~study) +
  ggtitle("Distribution by FWHN category  (Jaccard)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))


motion_rg = similarity_df %>% ggplot(aes(x = motion, y = jaccard, fill = motion, color = motion)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Motion category (Jaccard)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

modeltype_rg = similarity_df %>% ggplot(aes(x = model, y = jaccard, fill = model, color = model)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Model Type category  (Jaccard)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

contrast_rg = similarity_df %>% ggplot(aes(x = con, y = jaccard, fill = con, color = con)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Contrast category  (Jaccard)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.4.2 spearman

fwhm_rg = similarity_df %>% ggplot(aes(x = fwhm, y = spearman, fill = fwhm, color = fwhm)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by FWHN category  (spearman)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))


motion_rg = similarity_df %>% ggplot(aes(x = motion, y = spearman, fill = motion, color = motion)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Motion category (spearman)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

modeltype_rg = similarity_df %>% ggplot(aes(x = model, y = spearman, fill = model, color = model)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Model Type category  (spearman)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

contrast_rg = similarity_df %>% ggplot(aes(x = con, y = spearman, fill = con, color = con)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .1), width = 0.1
            )) +
  facet_grid(~study) +
  theme_classic() +
  ggtitle("Distribution by Contrast category  (spearman)") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

fwhm_rg

motion_rg

modeltype_rg

contrast_rg

3.5 Group Cohen’s ~ ICC

cohens_sim <- gather(similarity_df, key = "Run", value = "spearman", ses1_icc_cohensd:ses2_icc_cohensd) %>% 
  mutate(Run = case_when(
    Run == "ses1_icc_cohensd" ~ "Run1",
    Run == "ses_icc_cohensd" ~ "Run2",
    TRUE ~ Run
  ))
cohens_sim %>% ggplot(aes(x = fwhm, y = spearman, fill = fwhm, color = fwhm)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .2), width = 0.1
            )) +
  facet_grid(~study + Run) +
  theme_classic() +
  ggtitle("Distribution by FWHM category") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

cohens_sim %>% ggplot(aes(x = con, y = spearman, fill = con, color = con)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .2), width = 0.1
            )) +
  facet_grid(~study + Run) +
  theme_classic() +
  ggtitle("Distribution by Contrast category") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

cohens_sim %>% ggplot(aes(x = motion, y = spearman, fill = motion, color = motion)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .2), width = 0.1
            )) +
  facet_grid(~study + Run) +
  theme_classic() +
  ggtitle("Distribution by Motion category") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

cohens_sim %>% ggplot(aes(x = model, y = spearman, fill = model, color = model)) +
  geom_rain(alpha = .5, rain.side = 'l',
            boxplot.args = list(color = "black", outlier.shape = NA),
            boxplot.args.pos = list(
              position = ggpp::position_dodgenudge(x = .2), width = 0.1
            )) +
  facet_grid(~study + Run) +
  theme_classic() +
  ggtitle("Distribution by Model category") +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  guides(fill = 'none', color = 'none') +
  theme(text = element_text(family = "Times New Roman"),
        axis.text = element_text(size = 10, angle = 45, hjust = 1),
        axis.title = element_text(size = 10),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10),
        plot.title = element_text(size = 16))

4 Med/Min/Max Across Models

4.1 Subtreshold

cat("Subthreshold mask")
## Subthreshold mask
cat("\tICC, MSBS and MSWS median, miniumum and maximum")
##  ICC, MSBS and MSWS median, miniumum and maximum
calculate_summary_stats(icc_subthresh, median_est, "ICC")
study est_type median sd min max
abcd ICC 0.0795 0.0878043 0.003 0.321
ahrb ICC 0.1260 0.0878269 0.041 0.402
mls ICC 0.1310 0.0665285 0.037 0.320 
calculate_summary_stats(bs_subthresh, median_est, "MSBS")
study est_type median sd min max
abcd MSBS 0.8530 0.7354776 0.090 4.174
ahrb MSBS 0.2935 0.3249359 0.035 1.823
mls MSBS 0.6500 0.8990915 0.051 3.788 
calculate_summary_stats(ws_subthresh, median_est, "MSWS")
study est_type median sd min max
abcd MSWS 0.6275 0.4651668 0.084 2.386
ahrb MSWS 0.1900 0.1612192 0.027 0.932
mls MSWS 0.4665 0.4800625 0.041 2.074

4.2 Suprathreshold

cat("Suprathreshold mask")
## Suprathreshold mask
cat("\tICC, MSBS and MSWS median, miniumum and maximum")
##  ICC, MSBS and MSWS median, miniumum and maximum
calculate_summary_stats(icc_suprathresh, median_est, "ICC")
study est_type median sd min max
abcd ICC 0.1140 0.1143492 0.022 0.433
ahrb ICC 0.2090 0.1277304 0.043 0.532
mls ICC 0.2055 0.0969472 0.058 0.473 
calculate_summary_stats(bs_suprathresh, median_est, "MSBS")
study est_type median sd min max
abcd MSBS 0.6140 0.5201279 0.062 2.925
ahrb MSBS 0.1980 0.2168676 0.022 1.167
mls MSBS 0.4115 0.6058191 0.029 2.594 
calculate_summary_stats(ws_suprathresh, median_est, "MSWS")
study est_type median sd min max
abcd MSWS 0.432 0.3058086 0.056 1.636
ahrb MSWS 0.122 0.0986517 0.017 0.555
mls MSWS 0.280 0.2902648 0.024 1.235

4.3 simlarity

cat("Similarity median, minimum and maximum for jaccard and spearman")
## Similarity median, minimum and maximum for jaccard and spearman
calculate_summary_stats(similarity_df, jaccard, "Jaccard")
study est_type median sd min max
ahrb Jaccard 0.3037331 0.1853570 0.0395941 0.7281375
mls Jaccard 0.4178440 0.1234524 0.1979287 0.7410373 
calculate_summary_stats(similarity_df, spearman, "Spearman")
study est_type median sd min max
ahrb Spearman 0.8243898 0.2085670 0.3161175 0.9695884
mls Spearman 0.8712244 0.0910636 0.5911072 0.9723035

5 Specification Curve

Creating data in a format that is compatible with specr. Needs: estimate (i.e., ICC), std.error, conf.high, conf.low.

5.1 ICC

5.1.1 suprathreshold

5.1.1.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

# first, combine independent model vars into string to create average for each model type
icc_suprathresh$model_type <- paste(icc_suprathresh$fwhm, icc_suprathresh$motion,
                                    icc_suprathresh$con,  icc_suprathresh$model,
                                    sep = "_")

# calculate the avg estimate of ICC across study, standard error and +/- 95% confidence interval. In complete version
df_summ <- icc_suprathresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "supra-threshod ICC"
create_specr_plot(df_summ, est_label)

5.1.1.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "supra-threshold ICC"
create_specr_plot(df_summ_subset, est_label)

5.1.2 subthreshold

5.1.2.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

icc_subthresh$model_type <- paste(icc_subthresh$fwhm, icc_subthresh$motion,
                                  icc_subthresh$con,  icc_subthresh$model,
                                  sep = "_")

df_summ <- icc_subthresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "sub-threshold ICC"
create_specr_plot(df_summ, est_label)

5.1.2.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "sub-threshold ICC"
create_specr_plot(df_summ_subset, est_label)

5.2 MSBS

5.2.1 suprathreshold

5.2.1.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

bs_suprathresh$model_type <- paste(bs_suprathresh$fwhm, bs_suprathresh$motion,
                                   bs_suprathresh$con,  bs_suprathresh$model,
                                   sep = "_")

df_summ <- bs_suprathresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "supra-threshold MSBS"
create_specr_plot(df_summ, est_label)

5.2.1.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "supra-threshold MSBS"
create_specr_plot(df_summ_subset, est_label)

5.2.2 subthreshold

5.2.2.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

bs_subthresh$model_type <- paste(bs_subthresh$fwhm, bs_subthresh$motion,
                                 bs_subthresh$con,  bs_subthresh$model,
                                 sep = "_")


df_summ <- bs_subthresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "sub-threshold MSBS"
create_specr_plot(df_summ, est_label)

5.2.2.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "suprathreshold MSBS"
create_specr_plot(df_summ_subset, est_label)

5.3 MSWS

5.3.1 suprathreshold

5.3.1.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

ws_suprathresh$model_type <- paste(ws_suprathresh$fwhm, ws_suprathresh$motion,
                                   bs_suprathresh$con,  ws_suprathresh$model,
                                   sep = "_")

df_summ <- ws_suprathresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "supra-threshold MSWS"
create_specr_plot(df_summ, est_label)

5.3.1.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "supra-threshold MSWS"
create_specr_plot(df_summ_subset, est_label)

5.3.2 subthreshold

5.3.2.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

ws_subthresh$model_type <- paste(ws_subthresh$fwhm, ws_subthresh$motion,
                                 ws_subthresh$con,  ws_subthresh$model,
                                 sep = "_")

# calculate the avg estimate of ICC across study, standard error and +/- 95% confidence interval. In complete version
df_summ <- ws_subthresh %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(median_est), std.error = sd(median_est)/sqrt(length(median_est))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "sub-threshold MSWS"
create_specr_plot(df_summ, est_label)

5.3.2.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subplot
est_label = "sub-threshold MSWS"
create_specr_plot(df_summ_subset, est_label)

5.4 Similarity

5.4.1 Jaccard

5.4.1.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

similarity_df$model_type <- paste(similarity_df$fwhm, similarity_df$motion,
                                  similarity_df$con, similarity_df$model,
                                  sep = "_")

df_summ <- similarity_df %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(jaccard), std.error = sd(jaccard)/sqrt(length(jaccard))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "Jaccard"
create_specr_plot(df_summ, est_label)

5.4.1.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "Jaccard"
create_specr_plot(df_summ_subset, est_label)

5.4.2 Spearman

5.4.2.1 All models

creating combined panel 1 and panel 2 for all model permutations first.

similarity_df$model_type <- paste(similarity_df$fwhm, similarity_df$motion,
                                  similarity_df$con,  similarity_df$model, 
                                  sep = "_")

# calculate the avg estimate of ICC across study, standard error and +/- 95% confidence interval. In complete version
df_summ <- similarity_df %>% 
  group_by(model_type) %>% 
  summarise(estimate = mean(spearman), std.error = sd(spearman)/sqrt(length(spearman))) %>% 
  mutate(conf.low = estimate - 1.96 * std.error, conf.high = estimate + 1.96 * std.error) %>% 
  separate(col = model_type, into = c("fwhm","motion","contrast","model"), sep = "_|-", remove = FALSE)

# plot
est_label = "Spearman"
create_specr_plot(df_summ, est_label)

5.4.2.2 subset models

Creating model that is subset to visualize reliability for the top (>75th) and bottom (< 25th) quartile

# get 75th/25th qunatiles
top_75q = as.numeric(quantile(df_summ$estimate, .75))
bot_25q = as.numeric(quantile(df_summ$estimate, .25))
df_summ_subset = df_summ %>% 
  filter(estimate < bot_25q | estimate > top_75q)

# subset plots
est_label = "Spearman"
create_specr_plot(df_summ_subset, est_label)